Modern mammals originated after the end of the dinosaurs

A new and rapid computational approach to obtaining precisely dated evolutionary trees has confirmed that modern mammals originated after the extinction of the dinosaurs.

The authors, co-led by UCL (University College London) used the novel method to analyze a set of mammalian genomic data and answer an age-old question about whether groups of modern placental mammals originated before or after the Cretaceous mass extinction. Paleogene (K-Pg), which wiped out more than 70% of all species, including all dinosaurs.

The findings, published in Nature, confirm that the ancestors of modern placental mammal groups predate the K-Pg extinction that occurred 66 million years ago, resolving a controversy over the origins of modern mammals. Placental mammals are the most diverse group of living mammals and include groups such as primates, rodents, cetaceans, carnivores, chiropterans (bats), and humans.

Co-lead author Dr Sandra Álvarez-Carretero, from UCL’s Department of Genetics and Evolution, who began the research while at Queen Mary University of London, said in a statement: “By integrating entire genomes into the analysis and necessary fossil information, we were able to reduce uncertainties and obtain an accurate evolutionary timeline. Did modern mammal groups coexist with dinosaurs or did they originate after the mass extinction? Now we have a definitive answer ”.

Co-lead author Professor Phil Donoghue, University of Bristol, said: “The timeline of mammalian evolution is perhaps one of the most contentious topics in evolutionary biology. Early studies provided estimates of origin for modern placental groups deep in the Cretaceous, in the age of dinosaurs. Over the past two decades, studios have shifted back and forth between post-K-PG and pre-K-Pg diversification scenarios. Our precise schedule solves the problem. “

With sequencing projects around the world now producing hundreds or thousands of genome sequences, and with imminent plans to sequence more than a million species, evolutionary biologists will soon have a wealth of information at their fingertips. However, current methods for analyzing the vast sets of available genomic data and creating evolutionary timelines are inefficient and computationally expensive.

Co-lead author Dr Mario dos Reis from Queen Mary University of London said: “Inferring evolutionary timelines is a fundamental goal of biology. However, the most advanced methods are based on the use of computers to simulate evolutionary timelines and evaluate the most plausible ones. In our case, this was difficult due to the gigantic data set analyzed, which involves genetic data from almost 5,000 species of mammals and 72 complete genomes ”.

In this study, the researchers developed a new rapid Bayesian approach to analyze large numbers of genome sequences, while accounting for uncertainties within the data.

Dr. dos Reis added: “We solved the computational hurdles by dividing the analysis into substeps: first by simulating timelines using the 72 genomes and then using the results to guide the simulations in the remaining species. The use of genomes reduces the uncertainty because it allows the rejection of implausible timelines of the simulations ”.

Using their novel approach, the team was able to reduce the calculation time for this complex analysis from decades to months.

“If we had tried to analyze this large set of mammalian data on a supercomputer without using the Bayesian method we have developed, we would have had to wait decades to infer the mammalian time tree. Imagine how long this analysis could take if we used our own PCs, ”said Álvarez-Carretero.

“In addition, we were able to reduce the calculation time by a factor of 100. This new approach not only allows the analysis of genomic data sets, but also, being more efficient, substantially reduces the CO2 emissions released due to computation” .

The method developed in the study could be used to address other controversial evolutionary timelines that require the analysis of large data sets.

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